What is the difference between working on AI, using AI, creating AI agents, and creating AI automation?

Working on AI vs Using AI vs Creating AI Agents vs AI Automation

These represent different levels of engagement with AI technology, from end-user to creator.

The Four Roles

1. Using AI (End User)

textCopy
Tools: ChatGPT, Claude.ai, Gemini, Copilot, Cursor
Skills: Prompt writing, knowing what to ask
Example: "Write me a marketing email for our product launch"

Anyone can do this. No coding required.

2. Working on AI (ML Engineer / Researcher)

Building and improving AI models themselves:

pythonCopy
# Training, fine-tuning, evaluating models
from transformers import Trainer, TrainingArguments

trainer = Trainer(
    model=model,
    args=TrainingArguments(output_dir="./results", num_train_epochs=3),
    train_dataset=train_data,
    eval_dataset=eval_data
)
trainer.train()

Skills: PyTorch/TensorFlow, statistics, linear algebra, CUDA, distributed training Roles: ML Engineer, Research Scientist, Data Scientist

3. Creating AI Agents (Gen AI Engineer)

Building autonomous systems that use AI to complete tasks:

pythonCopy
from anthropic import Anthropic
from langchain.tools import tool

client = Anthropic()

@tool
def search_database(query: str) -> str:
    '''Search the product database'''
    return db.query(query)

@tool
def send_email(to: str, subject: str, body: str) -> str:
    '''Send an email'''
    return email_service.send(to, subject, body)

# Agent orchestrates tools autonomously
def run_support_agent(customer_query: str) -> str:
    response = client.messages.create(
        model="claude-opus-4-6",
        tools=[search_database, send_email],
        messages=[{"role": "user", "content": customer_query}]
    )
    return handle_agent_response(response)

Skills: AI APIs, LangChain/LangGraph, RAG, tool integration, prompt engineering Roles: Gen AI Engineer, AI Application Developer

4. AI Automation (no-code / low-code)

Using platforms to automate workflows without full software development:

textCopy
n8n workflow example:
  Trigger: New GitHub PR
      ↓
  AI Node: Claude reviews the PR
      ↓
  If: Issues found
      ↓
  GitHub: Post review comments
      ↓
  Slack: Notify team

Tools: n8n, Zapier, Make, LangFlow, Dify Skills: Workflow design, basic API knowledge, no-code platforms Roles: Business Analyst, Operations, Non-technical teams

How They Relate

textCopy
Researcher/ML Eng → builds foundation models (GPT, Claude, Llama)
                           ↓
Gen AI Engineer   → builds agents and apps using these models
                           ↓
Automation Eng    → builds workflows using AI APIs and no-code tools
                           ↓
End User          → uses the resulting tools (ChatGPT, Copilot, etc.)

Career Paths